I've been doing a project to determine the 'best' classifier for classification on a dataset from UCI. I used 10 fold stratified cross validation to calculate the mean accuracy. However it was suggested that I use ROC AUC instead.
My questions are:
1)Which is better cross validation or ROC?
2) Do you perform ROC on the test set, the training set or the whole dataset?
3) If it is the training set or test set. Do I perform it using each fold from the cross validation or should I just split the data once?